RSA2 Comprehensive Analysis Report: REACTOME Enrichment
This is a report made by Rayca Precision with the aim to analyze and interpret RNASeq data.
Enrichment Analysis of Genes and Ontologies by REACTOME
REACTOME is an open-source, open access, manually curated and peer-reviewed pathway database.
Link to Enriched REACTOME Pathways
The table below displays gene expression data sorted by adjusted p-values, allowing you to quickly identify the most significantly differentially expressed genes. In addition to the gene symbol and description, the table also includes links to the REACTOME page for each gene, which provides information about the specific metabolic pathways and biological processes in which the gene is involved. This can be particularly useful for understanding the biological context of the observed changes in gene expression and identifying potential targets for further investigation.
Note that the adjusted p-value represents the probability of obtaining a false positive result, so genes with lower adjusted p-values are more likely to be truly differentially expressed. Therefore, it is often useful to focus on genes with adjusted p-values below a certain threshold, such as 0.05 or 0.01, depending on the stringency of the analysis.
Overall, the table provides a valuable resource for exploring the differential expression of genes and the associated metabolic pathways and biological processes, and can facilitate the discovery of new insights and hypotheses in your research.
REACTOME is a database of curated biological pathways and processes that provides a wealth of information about specific metabolic routes. By sorting the table by the adjusted p-value, you can identify the pathways that are most significantly enriched in your data. To learn more about these pathways and their functions, you can click on the links in the “Links” column of the table to visit the corresponding REACTOME page. These pages contain detailed information about the pathway, including its description, associated genes, and links to other relevant resources.
Table 1: Table of Pathways for the Contrast: Treatment Sb Vs No.sb
Table 2: Table of Pathways for the Contrast: Treatment No.sb Vs Sb
Table 3: Table of Pathways for the Contrast: Time Day.2 Vs Day.0
Table 4: Table of Pathways for the Contrast: Temperature 37.C Vs 30.C
Enrichment Overview by LFC
An enrichment overview plot by log fold change (LFC) is a graphical representation of the gene sets that are significantly differentially expressed between two conditions in an RNA-seq experiment. The plot typically summarizes the top gene sets by displaying the log fold change of each set component for the contrast between the two conditions. The x-axis of the plot represents the log fold change of the gene set components, with positive values indicating upregulation and negative values indicating downregulation. The y-axis represents the gene sets, which are typically organized according to a specific category or annotation.
By plotting the log fold change of each gene set component, the enrichment overview plot allows you to identify the gene sets that are most significantly affected by the contrast between the two conditions. This can be useful for understanding the biological pathways or processes that are most affected by the contrast, and for identifying patterns of gene expression that may be relevant to your research question.
In conclusion, an enrichment overview plot by log fold change is a valuable tool for identifying differentially expressed gene sets in an RNA-seq experiment, and can help you understand the biological mechanisms underlying the observed changes in gene expression.
Enrichment overview plot by log fold change (LFC) is a powerful visualization tool that helps to identify significantly differentially expressed gene sets in an RNA-seq experiment.
Figure 1: Enrichment Overview of Treatment Sb Vs No.sb
Summarize the top genesets by displaying the logFC of each set components for the contrast: Treatment Sb Vs No.sb
Figure 2: Enrichment Overview of Treatment No.sb Vs Sb
Summarize the top genesets by displaying the logFC of each set components for the contrast: Treatment No.sb Vs Sb
Figure 3: Enrichment Overview of Time Day.2 Vs Day.0
Summarize the top genesets by displaying the logFC of each set components for the contrast: Time Day.2 Vs Day.0
Figure 4: Enrichment Overview of Temperature 37.C Vs 30.C
Summarize the top genesets by displaying the logFC of each set components for the contrast: Temperature 37.C Vs 30.C
Alluvial Plot
An alluvial plot is a powerful visualization tool that displays the relationships between different categories or groups of data. In the context of RNA-seq data analysis, it can be used to visualize the relationships between gene sets and the genes that belong to them.
In this plot, the gene sets are typically represented by horizontal “ribbons,” and the genes are represented by vertical “strips.” The overlap between the ribbons and strips indicates the relationships between the gene sets and genes.
For instance, an alluvial plot of treatment {X} and {Y} displays the relationships between gene sets and genes for the contrast between the two treatment conditions. By visualizing the overlap between gene sets and genes, the alluvial plot enables you to comprehend the relationships between the gene sets and the genes that belong to them. It also helps identify patterns of gene expression that may be relevant to your research question.
Overall, an alluvial plot is a useful tool for visualizing the relationships between gene sets and genes in an RNA-seq experiment. It can aid in comprehending the biological mechanisms underlying the observed changes in gene expression.
Figure 5: Alluvial Plot of Treatment Sb Vs No.sb
Display the relationship between genesets and genes thet belong to them for the contrast: Treatment Sb Vs No.sb
Figure 6: Alluvial Plot of Treatment No.sb Vs Sb
Display the relationship between genesets and genes thet belong to them for the contrast: Treatment No.sb Vs Sb
Figure 7: Alluvial Plot of Time Day.2 Vs Day.0
Display the relationship between genesets and genes thet belong to them for the contrast: Time Day.2 Vs Day.0
Figure 8: Alluvial Plot of Temperature 37.C Vs 30.C
Display the relationship between genesets and genes thet belong to them for the contrast: Temperature 37.C Vs 30.C
Enrichment Overview by Z-score
Enrichment overview plot by Z-score is a powerful visualization tool to identify the differentially expressed gene sets in an RNA-seq experiment. It shows the significance and direction of change (as measured by the Z-score) for each gene set for the contrast between the two conditions.
In this plot, the x-axis typically represents the Z-score, which is a measure of the statistical significance of the differential expression of each gene set. Positive and negative Z-scores indicate upregulation and downregulation, respectively. The y-axis represents the gene sets, which are usually organized according to a specific category or annotation.
The enrichment overview plot enables you to recognize the gene sets that are most significantly affected by the contrast between the two conditions by plotting the Z-score of each gene set. It can be useful for comprehending the biological pathways or processes that are most affected by the contrast, and for identifying patterns of gene expression that may be relevant to your research question.
In conclusion, Enrichment overview plot by Z-score is a valuable tool for understanding the biological mechanisms underlying the observed changes in gene expression and for identifying differentially expressed gene sets in an RNA-seq experiment.
Figure 9: Enrichment Overview by Z-score of Treatment Sb Vs No.sb
Obtain a simple overview of enrichment results, showing e.g. significance and direction of change (z_score) for the contrast: Treatment Sb Vs No.sb
Figure 10: Enrichment Overview by Z-score of Treatment No.sb Vs Sb
Obtain a simple overview of enrichment results, showing e.g. significance and direction of change (z_score) for the contrast: Treatment No.sb Vs Sb
Figure 11: Enrichment Overview by Z-score of Time Day.2 Vs Day.0
Obtain a simple overview of enrichment results, showing e.g. significance and direction of change (z_score) for the contrast: Time Day.2 Vs Day.0
Figure 12: Enrichment Overview by Z-score of Temperature 37.C Vs 30.C
Obtain a simple overview of enrichment results, showing e.g. significance and direction of change (z_score) for the contrast: Temperature 37.C Vs 30.C
Heatmap of Gene Sets-Genes
A heatmap of gene sets-genes for the contrast treatments is a graphical representation of the relationships between gene sets and the genes that belong to them, with the log fold change of each gene encoded as the color of the cell. The plot is useful for visualizing the overlap among gene sets and identifying patterns of differential gene expression between the different treatment conditions.
In this plot, the gene sets are typically represented by rows and the genes are represented by columns. The cells of the heatmap encode the log fold change of each gene, with positive and negative values indicating upregulation and downregulation, respectively. The color of the cells reflects the magnitude of the log fold change, with warmer colors (such as red and yellow) indicating higher expression levels and cooler colors (such as blue and green) indicating lower expression levels.
By visualizing the log fold change of each gene and the overlap among gene sets, the heatmap allows you to see the relationships between the gene sets and the genes that belong to them, and to identify patterns of gene expression that may be relevant to your research question.
A heatmap of gene sets-genes is a useful tool for visualizing the relationships between gene sets and genes in an RNA-seq experiment, and can help you understand the biological mechanisms underlying the observed changes in gene expression.
Figure 13: Heatmap of Gene Sets-Genes of Treatment Sb Vs No.sb
Plot a summary heatmap of genes vs genesets, ecoding the logFC of genes and representing the overlap among genesets for the contrast: Treatment Sb Vs No.sb
Figure 14: Heatmap of Gene Sets-Genes of Treatment No.sb Vs Sb
Plot a summary heatmap of genes vs genesets, ecoding the logFC of genes and representing the overlap among genesets for the contrast: Treatment No.sb Vs Sb
Figure 15: Heatmap of Gene Sets-Genes of Time Day.2 Vs Day.0
Plot a summary heatmap of genes vs genesets, ecoding the logFC of genes and representing the overlap among genesets for the contrast: Time Day.2 Vs Day.0
Figure 16: Heatmap of Gene Sets-Genes of Temperature 37.C Vs 30.C
Plot a summary heatmap of genes vs genesets, ecoding the logFC of genes and representing the overlap among genesets for the contrast: Temperature 37.C Vs 30.C
Graph of Gene Sets-Genes
A graph of gene sets-genes for a contrast is a graphical representation of the relationships between gene sets and the genes that belong to them. The plot is typically constructed as a bipartite graph, with gene sets and genes represented by two distinct sets of nodes that are connected by edges.
In this plot, the gene sets are typically represented by one set of nodes, and the genes are represented by another set of nodes. The edges between the nodes represent the relationships between the gene sets and the genes that belong to them. The direction and strength of the relationships can be represented in various ways, such as by the thickness or color of the edges.
By visualizing the relationships between gene sets and genes, the graph allows you to see the patterns of gene expression that are associated with each gene set, and to identify patterns of gene expression that may be relevant to your research question.
Overall, a graph of gene sets-genes is a useful tool for visualizing the relationships between gene sets and genes in an RNA-seq experiment, and can help you understand the biological mechanisms underlying the observed changes in gene expression. The specific content of the plot will depend on the data and the research question being addressed.
A graph of gene sets-genes is a powerful visualization tool that can help you identify key patterns in your data. Whether you are investigating gene expression in a disease model or exploring the effects of a treatment, a graph of gene sets-genes can provide valuable insights into the biological mechanisms at work. Consider using this tool to supplement your analysis and gain a deeper understanding of your RNA-seq data.
Figure17: Graph of Gene Sets-Genes of Contrast: Treatment Sb Vs No.sb
Display a bipartite graph where genesets and genes are included for the contrast: Treatment Sb Vs No.sb
Figure18: Graph of Gene Sets-Genes of Contrast: Treatment No.sb Vs Sb
Display a bipartite graph where genesets and genes are included for the contrast: Treatment No.sb Vs Sb
Figure19: Graph of Gene Sets-Genes of Contrast: Time Day.2 Vs Day.0
Display a bipartite graph where genesets and genes are included for the contrast: Time Day.2 Vs Day.0
Figure20: Graph of Gene Sets-Genes of Contrast: Temperature 37.C Vs 30.C
Display a bipartite graph where genesets and genes are included for the contrast: Temperature 37.C Vs 30.C
Radar Plots (Pairwise)
Radar plots, also known as spider plots or polar plots, are graphical representations of multivariate data in which the variables are plotted on separate axes that radiate out from the center of the plot. In the context of RNA-seq data analysis, radar plots can be used to visualize the response within biological processes of contrasts, or the differences in gene expression between different conditions. In the case of radar plots of contrasts, the plot would likely show the differences in gene expression between two or more conditions. The biological processes or pathways represented by the plot would be plotted on separate axes, with the gene expression levels for each condition plotted on each axis.
By visualizing the gene expression levels for each condition on each axis, the radar plot allows you to see the patterns of gene expression that are associated with each biological process, and to identify the processes that are most affected by the contrast between the conditions.
Radar plots are a useful tool for visualizing multivariate data in an RNA-seq experiment. They allow you to see the patterns of gene expression associated with each biological process and to identify the most affected processes.
Figure 21: Radar Plot of Contrasts Treatment Sb Vs No.sb and Treatment No.sb Vs Sb
Plot displaying the response within biological processes of contrasts: Treatment Sb Vs No.sb and Treatment No.sb Vs Sb
Figure 22: Radar Plot of Contrasts Treatment Sb Vs No.sb and Time Day.2 Vs Day.0
Plot displaying the response within biological processes of contrasts: Treatment Sb Vs No.sb and Time Day.2 Vs Day.0
Figure 23: Radar Plot of Contrasts Treatment Sb Vs No.sb and Temperature 37.C Vs 30.C
Plot displaying the response within biological processes of contrasts: Treatment Sb Vs No.sb and Temperature 37.C Vs 30.C
Figure 24: Radar Plot of Contrasts Treatment No.sb Vs Sb and Time Day.2 Vs Day.0
Plot displaying the response within biological processes of contrasts: Treatment No.sb Vs Sb and Time Day.2 Vs Day.0
Figure 25: Radar Plot of Contrasts Treatment No.sb Vs Sb and Temperature 37.C Vs 30.C
Plot displaying the response within biological processes of contrasts: Treatment No.sb Vs Sb and Temperature 37.C Vs 30.C
Figure 26: Radar Plot of Contrasts Time Day.2 Vs Day.0 and Temperature 37.C Vs 30.C
Plot displaying the response within biological processes of contrasts: Time Day.2 Vs Day.0 and Temperature 37.C Vs 30.C
Meta Table of Gene Sets
A meta table of gene sets that includes GO (Gene Ontology) annotations, gene set ID, gene set description, gene set p-value, and gene set genes is a summary table that lists the gene sets that have been identified in an RNA-seq experiment, along with relevant information about each gene set. The specific content of the table will depend on the data and the research question being addressed, but it might include the following types of information:
- GO (Gene Ontology) annotations: The Gene Ontology (GO) is a standardized vocabulary for annotating genes and gene products. The GO annotations for a gene set indicate the biological processes, cellular components, or molecular functions that are associated with the genes in the set.
- Gene set ID: A unique identifier for the gene set, such as a numerical label or a code.
- Gene set description: A short description or summary of the gene set, explaining what it represents and why it is relevant to the research question.
- Gene set p-value: The p-value of the gene set, which is a measure of the statistical significance of the differential expression of the gene set between the conditions being compared. A lower p-value indicates a higher level of statistical significance.
- Gene set genes: A list of the genes that belong to the gene set, typically presented as a comma-separated list or in a tabular format.
Table 1: Table of Genesets-Genes for the Contrast: Treatment Sb Vs No.sb
Table 2: Table of Genesets-Genes for the Contrast: Treatment No.sb Vs Sb
Table 3: Table of Genesets-Genes for the Contrast: Time Day.2 Vs Day.0
Table 4: Table of Genesets-Genes for the Contrast: Temperature 37.C Vs 30.C
Drug - Gene Plots
In the field of pharmacogenomics, drug-gene interactions play a critical role in determining the efficacy and safety of a drug. Understanding the relationships between drugs and genes can help researchers identify potential drug targets, optimize drug dosages, and predict adverse drug reactions.
One useful tool for visualizing drug-gene interactions is the Drug-Gene plot. These plots typically consist of a network of genes and drugs, with edges connecting the drugs to the genes that they target. The direction and strength of the relationships between drugs and genes can be represented in various ways, such as by the thickness or color of the edges.
Within the realm of Drug-Gene plots, there are two commonly used plot types: gene-drugs graph plots and alluvial plots. Gene-drugs graph plots display the relationships between drugs and genes in a network format, with drugs and genes represented by nodes and the edges between them indicating the interactions. In contrast, alluvial plots depict the flow of genes from one state to another, such as from unexpressed to overexpressed, in response to different drugs or doses.
Drug-Gene plots can be used to identify potential drug targets and biomarkers for drug response or toxicity, as well as to better understand the underlying biological mechanisms of drug action. In this section, we will explore the use of gene-drugs graph plots and alluvial plots in visualizing drug-gene interactions in RNA-seq data.
Drug - Gene Graphs
A gene-drug graph plot is a graphical representation of the relationships between genes and drugs based on their differential expression profiles. In this plot, genes and drugs are represented as nodes, with edges connecting them based on their correlation. The thickness and color of the edges can represent the strength and direction of the correlation.
The gene-drug graph plot can help you identify potential drug targets based on the genes that are differentially expressed in a particular condition. By exploring the relationships between genes and drugs, you can gain insights into the mechanisms underlying the observed changes in gene expression, and potentially identify new therapeutic targets.
Figure 27: Graph of Genes-Drugs for Contrast: Treatment Sb Vs No.sb
Display a bipartite graph where drugs and genes are included for the contrast: Treatment Sb Vs No.sb
Figure 28: Graph of Genes-Drugs for Contrast: Treatment No.sb Vs Sb
Display a bipartite graph where drugs and genes are included for the contrast: Treatment No.sb Vs Sb
Figure 29: Graph of Genes-Drugs for Contrast: Time Day.2 Vs Day.0
Display a bipartite graph where drugs and genes are included for the contrast: Time Day.2 Vs Day.0
Figure 30: Graph of Genes-Drugs for Contrast: Temperature 37.C Vs 30.C
Display a bipartite graph where drugs and genes are included for the contrast: Temperature 37.C Vs 30.C
Drug - Gene Alluvial Plots
A gene-drug alluvial plot is a graphical representation of the relationships between genes and drugs based on their differential expression profiles, using a type of Sankey diagram. In this plot, genes and drugs are represented as sets of nodes that are connected by ribbons representing the correlation between them. The thickness of the ribbons can represent the strength of the correlation.
The gene-drug alluvial plot can help you identify potential drug targets based on the genes that are differentially expressed in a particular condition. By exploring the relationships between genes and drugs, you can gain insights into the mechanisms underlying the observed changes in gene expression, and potentially identify new therapeutic targets. The alluvial plot also allows for the comparison of the relationships between genes and drugs in different conditions, which can help identify patterns of drug response and potentially guide treatment decisions.
Figure 31: Alluvial Plot of Genes-Drugs for Contrast: Treatment Sb Vs No.sb
Display a graph where action type, drugs and genes are connected for the contrast: Treatment Sb Vs No.sb
Figure 31: Alluvial Plot of Genes-Drugs for Contrast: Treatment No.sb Vs Sb
Display a graph where action type, drugs and genes are connected for the contrast: Treatment No.sb Vs Sb
Figure 31: Alluvial Plot of Genes-Drugs for Contrast: Time Day.2 Vs Day.0
Display a graph where action type, drugs and genes are connected for the contrast: Time Day.2 Vs Day.0
Figure 31: Alluvial Plot of Genes-Drugs for Contrast: Temperature 37.C Vs 30.C
Display a graph where action type, drugs and genes are connected for the contrast: Temperature 37.C Vs 30.C
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